
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "137"
csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 23 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_
Found 21 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
            antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
        else:
            antnum = int(row['Ant'])
        if antnum != int(antenna):
            continue
        
        if np.issubdtype(type(row['Node']), np.integer):
            row['Node'] = str(row['Node'])
        if type(row['Node']) == str and row['Node'].isnumeric():
            row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


htmls_to_display = []
for am_html in auto_metric_htmls:
    html_to_display = ''
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line:
            htmls_to_display.append(html_to_display)
            break


for i, html_to_display in enumerate(htmls_to_display):
    if i == 100:
        break
    display(HTML(html_to_display))

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459838	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.457927	-0.450554	12.596780	5.246500	5.068760	7.689074	-0.768105	-0.028885	0.7472	0.7185	0.4166	0.000000	0.000000
2459836	RF_maintenance	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6986	0.6077	0.4390	nan	nan
2459835	RF_maintenance	100.00%	0.00%	0.00%	0.00%	-	-	4.981373	5.662762	11.023864	8.230705	20.905781	55.697424	-4.910455	2.021261	0.7931	0.4979	0.5825	nan	nan
2459833	RF_maintenance	100.00%	0.00%	0.00%	0.00%	-	-	16.746938	19.295406	43.675101	37.296141	203.014815	288.211100	13.629661	24.770367	0.8043	0.4799	0.5873	nan	nan
2459832	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.393076	0.466526	10.500774	4.843067	5.829039	4.505292	-0.334483	0.059918	0.8161	0.5449	0.5795	4.345109	3.337515
2459831	RF_maintenance	100.00%	100.00%	44.89%	0.00%	-	-	0.211426	3.305664	19.645622	20.107325	2.451236	1.781269	1.429380	-0.314960	0.1368	0.2673	0.0763	nan	nan
2459830	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	8.323896	0.329481	14.644436	2.585410	14.618307	9.289838	-2.042536	2.803394	0.8159	0.5598	0.5576	8.440643	6.158060
2459829	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	7.120143	-0.681242	14.942373	-0.548944	12.623561	45.063343	1.446221	9.958486	0.7574	0.6674	0.4191	0.000000	0.000000
2459828	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	14.925575	4.600172	18.618027	9.476191	30.841597	6.647719	-0.209175	-2.154293	0.7890	0.5646	0.5312	3.582583	3.095136
2459827	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	11.690163	2.892330	30.930034	16.148202	20.356436	2.160348	-2.708302	-1.612549	0.7311	0.6628	0.4356	5.792979	5.130745
2459826	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	14.771131	6.747419	24.817210	16.036975	40.838561	10.070244	0.322144	1.583744	0.7732	0.5299	0.5397	3.328310	2.459322
2459825	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	16.079293	11.514746	19.152879	15.864766	22.749394	14.074417	-0.912488	-1.068595	0.7804	0.5506	0.5294	3.856257	3.343532
2459824	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	8.854069	4.770393	26.972228	22.278677	7.505943	4.339083	-2.282638	-2.049028	0.6900	0.7205	0.3974	4.542329	4.567891
2459823	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	19.256452	15.228876	20.833688	18.238692	27.638700	24.750522	14.786747	11.335449	0.7396	0.6219	0.4912	3.780346	3.995202
2459822	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	17.681755	13.972495	23.238947	20.146229	26.018190	18.959964	-0.279821	-0.394298	0.7649	0.5470	0.5392	3.687310	3.108239
2459821	RF_maintenance	100.00%	34.95%	100.00%	0.00%	100.00%	0.00%	23.226057	28.873454	14.656253	38.481437	22.639165	25.859016	1.063325	-0.602923	0.4196	0.0445	0.2181	3.318695	1.288779
2459820	RF_maintenance	100.00%	63.37%	100.00%	0.00%	100.00%	0.00%	26.091695	24.094179	10.568169	30.521271	38.820540	69.694544	26.249844	7.275945	0.3602	0.0518	0.1973	2.841444	1.234537
2459817	RF_maintenance	100.00%	67.74%	100.00%	0.00%	100.00%	0.00%	20.917403	56.059754	19.171206	36.262767	34.841354	37.495307	0.198837	1.840740	0.4045	0.0674	0.2173	3.749730	1.335641
2459816	RF_maintenance	100.00%	25.94%	100.00%	0.00%	100.00%	0.00%	14.534489	39.947192	28.710872	36.386551	44.390695	48.282402	-4.090170	9.162276	0.4295	0.0658	0.2573	4.063054	1.345583
2459815	RF_maintenance	100.00%	54.30%	100.00%	0.00%	100.00%	0.00%	20.196463	29.462108	21.680279	40.867496	45.356908	48.390769	6.691674	13.923821	0.4415	0.0558	0.2720	3.512073	1.292756
2459814	RF_maintenance	0.00%	-	-	-	-	-	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
2459813	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	30.791901	33.602669	24.451008	25.267891	84.149978	85.783047	7.603676	11.908807	0.0369	0.0473	0.0062	0.844629	0.849194

Antenna Season Report Notebook¶

Antenna 137 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 137, Node N07:

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 137: 2459838

Antenna 137: 2459835

Antenna 137: 2459833

Antenna 137: 2459832

Antenna 137: 2459831

Antenna 137: 2459830

Antenna 137: 2459829

Antenna 137: 2459828

Antenna 137: 2459827

Antenna 137: 2459826

Antenna 137: 2459825

Antenna 137: 2459824

Antenna 137: 2459823

Antenna 137: 2459822

Antenna 137: 2459821

Antenna 137: 2459820

Antenna 137: 2459817

Antenna 137: 2459816

Antenna 137: 2459815

Antenna 137: 2459814

Antenna 137: 2459813

Antenna Season Report Notebook¶

Antenna 137 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 137, Node N07:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 137: 2459838

Antenna 137: 2459835

Antenna 137: 2459833

Antenna 137: 2459832

Antenna 137: 2459831

Antenna 137: 2459830

Antenna 137: 2459829

Antenna 137: 2459828

Antenna 137: 2459827

Antenna 137: 2459826

Antenna 137: 2459825

Antenna 137: 2459824

Antenna 137: 2459823

Antenna 137: 2459822

Antenna 137: 2459821

Antenna 137: 2459820

Antenna 137: 2459817

Antenna 137: 2459816

Antenna 137: 2459815

Antenna 137: 2459814

Antenna 137: 2459813

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶